Łukasz Kidziński, Stanford University: Using Deep Learning to Predict the Outcomes of Surgery

By
Katie Pollitt,
Global Summit Creator
- RE•WORK
March 02, 2018

Recent advancements in artificial intelligence in have led to breakthroughs in healthcare from medical imaging for disease identification, to predictive analytics for health forecasting and predictions. Most recently, Alphabet have studied how deep learning could help predict heart disease risk of patients based on results achieved from eye scans.

Deep learning is incredibly powerful when it comes to handling large amounts of data. At the Mobilize Centre at Stanford, Researcher Łukasz Kidziński and his team are on a mission to tackle key bottlenecks in data science for biomedical research. They do this through analysing movement data from wearable sensors, smartphones, clinics and research labs to better understand and improve human movement. Similar to Alphabet, Łukasz, is currently working on predicting models for disease progression and surgery outcomes, as well as new methods for collecting movement data at scale.

They aim also to predict surgical outcomes, which can be very challenging, as they depend multitude of factors from which we can measure only a few. In such settings, machine learning methods usually require lots of observations and availability of quality data is often a bottleneck. This problem is particularly severe when we are interested in designing new or personalized surgeries. Then, we have seemingly no data, while the room for experimentation is obviously very limited.

Łukasz explained that Healthcare is particularly interesting when it comes to deep learning, not only because of the importance of projects but also because of the abundance of data collected in clinics and research labs. Yet, many medical pipelines still rely on very old technologies so there is a lot of room for innovation in both data collection and analysis, so the opportunity for progress is huge.

At the Mobilize Centre at Stanford, they’re using AI for a positive impact for ‘predicting how a brain will adapt to a new configuration of as musculoskeletal system following an orthopedic surgery. Imagine we want to predict how a person will walk after a surgeon rotates a bone. While we can model the musculoskeletal system with our current understanding of human body, it is difficult to predict how the brain will control it. AI potentially gives us an opportunity to approach this problem.’ Recently, advances in deep reinforcement learning have allowed research to progress - these are the same techniques that helped DeepMind win with the world champion in Go and to build controllers for robots from scratch, allow me to study the motor control unit in a human brain.

Łukasz shared his enthusiasm for the developments of AI in the coming years:

‘I believe that in the short term the most important advances for industry come from AI in image processing. Mimicking human perception will have great implications in healthcare, quality control, security, entertainment, sports, and lots of other disciplines. In the long term, we may see optimization techniques (such as deep reinforcement learning) finding control strategies beyond human performance.’